Wednesday, April 15, 2015

Phys.org comments on MIT method to use patent citation statistics

Work of Chris Benson and Chris Magee using patent citation statistics was recently published in PLoS ONE.

link to phys.org article

http://phys.org/news/2015-04-method-patent-technology-future.html

The team devised a
simple equation incorporating forward citation and publication date, and
used the method to predict improvement rates for 28 technologies. The
researchers then compared the rates with those they previously obtained
using their more time-intensive, historical data-based approach, and
found the results from both methods matched closely.
They then used their more efficient approach to predict the
improvement rates of 11 emerging technologies in the next 10 years.
Among these, the fastest-growing domains appear to be online learning
and digital representation, while slower technologies include food
engineering and nuclear fusion.
Magee hopes the method may be used much like a rating system, similar
to Standard & Poor's and other stock-market indices. Such ratings
could be useful for investors looking for the next big breakthrough, as
well as scientific labs that are contemplating new research directions.
Magee says knowing how various technologies may improve in the next
decade could give innovators an idea of when "feeder technologies" may
mature, and enable more pie-in-the-sky ideas, like mass-produced
hoverboards and flying cars.
"We can help reduce the uncertainty of the capabilities of a
technology in the future, not to zero, but to a more manageable number,"
Benson says. "I believe that's valuable in a lot of different ways."

Now engineers at
MIT have devised a formula for estimating how fast a technology is
advancing, based on information gleaned from relevant patents.
The researchers determined the improvement rates of 28 different
technologies, including solar photovoltaics, 3-D printing, fuel-cell
technology, and genome sequencing. They searched through the U.S. Patent
Office database for patents associated with each domain—more than
500,000 total—by developing a novel method to quickly and accurately
select the patents that best represent each technology.
Once these were identified, the researchers analyzed certain metrics
across patents in each domain, and found that some were more likely to
predict a technology's improvement rate than others. In particular,
forward citations—the number of times a patent is cited by subsequent
patents—is a good predictor, as is the date of a patent's publication:
Technologies with more recent patents are likely innovating at a faster
rate than those with older patents.
The team devised an equation incorporating a patent set's average
forward citation and average publication date, and calculated the rate
of improvement for each technology domain. Their results matched closely
with the rates determined through the more labor-intensive approach of
finding numerous historical performance data points for each technology.
Among the 28 domains analyzed, the researchers found the
fastest-developing technologies include optical and wireless
communications, 3-D printing, and MRI technology, while domains such as
batteries, wind turbines, and combustion engines appear to be improving at slower rates.
Chris Benson, a former graduate student in MIT's Department of
Mechanical Engineering, says the new prediction tool may be of interest
to venture capitalists, startups, and government and industry labs
looking to explore new technology.

Now engineers at
MIT have devised a formula for estimating how fast a technology is
advancing, based on information gleaned from relevant patents.
The researchers determined the improvement rates of 28 different
technologies, including solar photovoltaics, 3-D printing, fuel-cell
technology, and genome sequencing. They searched through the U.S. Patent
Office database for patents associated with each domain—more than
500,000 total—by developing a novel method to quickly and accurately
select the patents that best represent each technology.
Once these were identified, the researchers analyzed certain metrics
across patents in each domain, and found that some were more likely to
predict a technology's improvement rate than others. In particular,
forward citations—the number of times a patent is cited by subsequent
patents—is a good predictor, as is the date of a patent's publication:
Technologies with more recent patents are likely innovating at a faster
rate than those with older patents.
The team devised an equation incorporating a patent set's average
forward citation and average publication date, and calculated the rate
of improvement for each technology domain. Their results matched closely
with the rates determined through the more labor-intensive approach of
finding numerous historical performance data points for each technology.
Among the 28 domains analyzed, the researchers found the
fastest-developing technologies include optical and wireless
communications, 3-D printing, and MRI technology, while domains such as
batteries, wind turbines, and combustion engines appear to be improving at slower rates.
Chris Benson, a former graduate student in MIT's Department of
Mechanical Engineering, says the new prediction tool may be of interest
to venture capitalists, startups, and government and industry labs
looking to explore new technology.

Now engineers at
MIT have devised a formula for estimating how fast a technology is
advancing, based on information gleaned from relevant patents.
The researchers determined the improvement rates of 28 different
technologies, including solar photovoltaics, 3-D printing, fuel-cell
technology, and genome sequencing. They searched through the U.S. Patent
Office database for patents associated with each domain—more than
500,000 total—by developing a novel method to quickly and accurately
select the patents that best represent each technology.
Once these were identified, the researchers analyzed certain metrics
across patents in each domain, and found that some were more likely to
predict a technology's improvement rate than others. In particular,
forward citations—the number of times a patent is cited by subsequent
patents—is a good predictor, as is the date of a patent's publication:
Technologies with more recent patents are likely innovating at a faster
rate than those with older patents.
The team devised an equation incorporating a patent set's average
forward citation and average publication date, and calculated the rate
of improvement for each technology domain. Their results matched closely
with the rates determined through the more labor-intensive approach of
finding numerous historical performance data points for each technology.
Among the 28 domains analyzed, the researchers found the
fastest-developing technologies include optical and wireless
communications, 3-D printing, and MRI technology, while domains such as
batteries, wind turbines, and combustion engines appear to be improving at slower rates.
Chris Benson, a former graduate student in MIT's Department of
Mechanical Engineering, says the new prediction tool may be of interest
to venture capitalists, startups, and government and industry labs
looking to explore new technology.

Now engineers at
MIT have devised a formula for estimating how fast a technology is
advancing, based on information gleaned from relevant patents.
The researchers determined the improvement rates of 28 different
technologies, including solar photovoltaics, 3-D printing, fuel-cell
technology, and genome sequencing. They searched through the U.S. Patent
Office database for patents associated with each domain—more than
500,000 total—by developing a novel method to quickly and accurately
select the patents that best represent each technology.
Once these were identified, the researchers analyzed certain metrics
across patents in each domain, and found that some were more likely to
predict a technology's improvement rate than others. In particular,
forward citations—the number of times a patent is cited by subsequent
patents—is a good predictor, as is the date of a patent's publication:
Technologies with more recent patents are likely innovating at a faster
rate than those with older patents.
The team devised an equation incorporating a patent set's average
forward citation and average publication date, and calculated the rate
of improvement for each technology domain. Their results matched closely
with the rates determined through the more labor-intensive approach of
finding numerous historical performance data points for each technology.
Among the 28 domains analyzed, the researchers found the
fastest-developing technologies include optical and wireless
communications, 3-D printing, and MRI technology, while domains such as
batteries, wind turbines, and combustion engines appear to be improving at slower rates.
Chris Benson, a former graduate student in MIT's Department of
Mechanical Engineering, says the new prediction tool may be of interest
to venture capitalists, startups, and government and industry labs
looking to explore new technology.

Now engineers at
MIT have devised a formula for estimating how fast a technology is
advancing, based on information gleaned from relevant patents.
The researchers determined the improvement rates of 28 different
technologies, including solar photovoltaics, 3-D printing, fuel-cell
technology, and genome sequencing. They searched through the U.S. Patent
Office database for patents associated with each domain—more than
500,000 total—by developing a novel method to quickly and accurately
select the patents that best represent each technology.
Once these were identified, the researchers analyzed certain metrics
across patents in each domain, and found that some were more likely to
predict a technology's improvement rate than others. In particular,
forward citations—the number of times a patent is cited by subsequent
patents—is a good predictor, as is the date of a patent's publication:
Technologies with more recent patents are likely innovating at a faster
rate than those with older patents.
The team devised an equation incorporating a patent set's average
forward citation and average publication date, and calculated the rate
of improvement for each technology domain. Their results matched closely
with the rates determined through the more labor-intensive approach of
finding numerous historical performance data points for each technology.
Among the 28 domains analyzed, the researchers found the
fastest-developing technologies include optical and wireless
communications, 3-D printing, and MRI technology, while domains such as
batteries, wind turbines, and combustion engines appear to be improving at slower rates.
Chris Benson, a former graduate student in MIT's Department of
Mechanical Engineering, says the new prediction tool may be of interest
to venture capitalists, startups, and government and industry labs
looking to explore new technology.

Now engineers at
MIT have devised a formula for estimating how fast a technology is
advancing, based on information gleaned from relevant patents.
The researchers determined the improvement rates of 28 different
technologies, including solar photovoltaics, 3-D printing, fuel-cell
technology, and genome sequencing. They searched through the U.S. Patent
Office database for patents associated with each domain—more than
500,000 total—by developing a novel method to quickly and accurately
select the patents that best represent each technology.
Once these were identified, the researchers analyzed certain metrics
across patents in each domain, and found that some were more likely to
predict a technology's improvement rate than others. In particular,
forward citations—the number of times a patent is cited by subsequent
patents—is a good predictor, as is the date of a patent's publication:
Technologies with more recent patents are likely innovating at a faster
rate than those with older patents.
The team devised an equation incorporating a patent set's average
forward citation and average publication date, and calculated the rate
of improvement for each technology domain. Their results matched closely
with the rates determined through the more labor-intensive approach of
finding numerous historical performance data points for each technology.
Among the 28 domains analyzed, the researchers found the
fastest-developing technologies include optical and wireless
communications, 3-D printing, and MRI technology, while domains such as
batteries, wind turbines, and combustion engines appear to be improving at slower rates.
Chris Benson, a former graduate student in MIT's Department of
Mechanical Engineering, says the new prediction tool may be of interest
to venture capitalists, startups, and government and industry labs
looking to explore new technology.

Now engineers at
MIT have devised a formula for estimating how fast a technology is
advancing, based on information gleaned from relevant patents.
The researchers determined the improvement rates of 28 different
technologies, including solar photovoltaics, 3-D printing, fuel-cell
technology, and genome sequencing. They searched through the U.S. Patent
Office database for patents associated with each domain—more than
500,000 total—by developing a novel method to quickly and accurately
select the patents that best represent each technology.
Once these were identified, the researchers analyzed certain metrics
across patents in each domain, and found that some were more likely to
predict a technology's improvement rate than others. In particular,
forward citations—the number of times a patent is cited by subsequent
patents—is a good predictor, as is the date of a patent's publication:
Technologies with more recent patents are likely innovating at a faster
rate than those with older patents.
The team devised an equation incorporating a patent set's average
forward citation and average publication date, and calculated the rate
of improvement for each technology domain. Their results matched closely
with the rates determined through the more labor-intensive approach of
finding numerous historical performance data points for each technology.
Among the 28 domains analyzed, the researchers found the
fastest-developing technologies include optical and wireless
communications, 3-D printing, and MRI technology, while domains such as
batteries, wind turbines, and combustion engines appear to be improving at slower rates.
Chris Benson, a former graduate student in MIT's Department of
Mechanical Engineering, says the new prediction tool may be of interest
to venture capitalists, startups, and government and industry labs
looking to explore new technology.

Now engineers at MIT
have devised a formula for estimating how fast a technology is
advancing, based on information gleaned from relevant patents.
The researchers determined the improvement rates of 28 different
technologies, including solar photovoltaics, 3-D printing, fuel-cell
technology, and genome sequencing. They searched through the U.S. Patent
Office database for patents associated with each domain—more than
500,000 total—by developing a novel method to quickly and accurately
select the patents that best represent each technology.
Once these were identified, the researchers analyzed certain metrics
across patents in each domain, and found that some were more likely to
predict a technology's improvement rate than others. In particular,
forward citations—the number of times a patent is cited by subsequent
patents—is a good predictor, as is the date of a patent's publication:
Technologies with more recent patents are likely innovating at a faster
rate than those with older patents.
The team devised an equation incorporating a patent set's average
forward citation and average publication date, and calculated the rate
of improvement for each technology domain. Their results matched closely
with the rates determined through the more labor-intensive approach of
finding numerous historical performance data points for each technology.
Among the 28 domains analyzed, the researchers found the
fastest-developing technologies include optical and wireless
communications, 3-D printing, and MRI technology, while domains such as
batteries, wind turbines, and combustion engines appear to be improving at slower rates.
Chris Benson, a former graduate student in MIT's Department of
Mechanical Engineering, says the new prediction tool may be of interest
to venture capitalists, startups, and government and industry labs
looking to explore new technology.

Now engineers at MIT
have devised a formula for estimating how fast a technology is
advancing, based on information gleaned from relevant patents.
The researchers determined the improvement rates of 28 different
technologies, including solar photovoltaics, 3-D printing, fuel-cell
technology, and genome sequencing. They searched through the U.S. Patent
Office database for patents associated with each domain—more than
500,000 total—by developing a novel method to quickly and accurately
select the patents that best represent each technology.
Once these were identified, the researchers analyzed certain metrics
across patents in each domain, and found that some were more likely to
predict a technology's improvement rate than others. In particular,
forward citations—the number of times a patent is cited by subsequent
patents—is a good predictor, as is the date of a patent's publication:
Technologies with more recent patents are likely innovating at a faster
rate than those with older patents.
The team devised an equation incorporating a patent set's average
forward citation and average publication date, and calculated the rate
of improvement for each technology domain. Their results matched closely
with the rates determined through the more labor-intensive approach of
finding numerous historical performance data points for each technology.
Among the 28 domains analyzed, the researchers found the
fastest-developing technologies include optical and wireless
communications, 3-D printing, and MRI technology, while domains such as
batteries, wind turbines, and combustion engines appear to be improving at slower rates.
Chris Benson, a former graduate student in MIT's Department of
Mechanical Engineering, says the new prediction tool may be of interest
to venture capitalists, startups, and government and industry labs
looking to explore new technology.

Now engineers at MIT
have devised a formula for estimating how fast a technology is
advancing, based on information gleaned from relevant patents.
The researchers determined the improvement rates of 28 different
technologies, including solar photovoltaics, 3-D printing, fuel-cell
technology, and genome sequencing. They searched through the U.S. Patent
Office database for patents associated with each domain—more than
500,000 total—by developing a novel method to quickly and accurately
select the patents that best represent each technology.
Once these were identified, the researchers analyzed certain metrics
across patents in each domain, and found that some were more likely to
predict a technology's improvement rate than others. In particular,
forward citations—the number of times a patent is cited by subsequent
patents—is a good predictor, as is the date of a patent's publication:
Technologies with more recent patents are likely innovating at a faster
rate than those with older patents.
The team devised an equation incorporating a patent set's average
forward citation and average publication date, and calculated the rate
of improvement for each technology domain. Their results matched closely
with the rates determined through the more labor-intensive approach of
finding numerous historical performance data points for each technology.
Among the 28 domains analyzed, the researchers found the
fastest-developing technologies include optical and wireless
communications, 3-D printing, and MRI technology, while domains such as
batteries, wind turbines, and combustion engines appear to be improving at slower rates.
Chris Benson, a former graduate student in MIT's Department of
Mechanical Engineering, says the new prediction tool may be of interest
to venture capitalists, startups, and government and industry labs
looking to explore new technology.

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About Me

I'm a patent lawyer located in central New Jersey. I have a J.D. from the University of Chicago and a Ph.D. from Stanford University, where I studied graphite intercalation compounds at the Center for Materials Research. I worked at Exxon Corporate Research in areas ranging from engine deposits through coal and petroleum to fullerenes. An article that I wrote in The Trademark Reporter, 1994, 84, 379-407 on color trademarks was cited by Supreme Court in Qualitex v. Jacobson, 514 US 159 (1995) and the methodology was adopted
in the Capri case in N.D. Ill. An article that I wrote on DNA profiling was cited by the Colorado Supreme Court (Shreck case) and a Florida appellate court (Brim case). I was interviewed by NHK-TV about the Jan-Hendrik Schon affair. I am developing ipABC, an entity that combines rigorous IP analytics with study of business models, to optimize utilization of intellectual property. I can be reached at C8AsF5 at yahoo.com.